Central composite design adoption for assessing the tio quality using response surface method

Stability is a major issue in every nanolubricant. The UV visible spectrophotometry approach is one method for assessing the dispersion quality standard of a nanolubricant. UV visible spectrophotometry is adopted to determine the absorbance level of a nanolubricant. This method assesses how well a nanolubricant absorbs UV rays emitted by a light source. A central composite design based on surface response was used to assess the influence of concentration and standing time on the absorbance ratio of TiO2-POE nanolubricant. The TiO2-POE sample was synthesized in two steps with a 0.02-0.2 vol% concentration range. A homogenizer was used to ultrasonicate the samples for 80 min. Then, U.V. visible spectrophotometry was used to examine the absorbance ratio of each sample from day 1 to day 15. Sixteen runs were performed to comply with a quadratic design for experimental data collection, then fitted using face canter alpha. The ANOVA analysis revealed that the experimental data fit the polynomial model, with an R2 value of 0.9902 and a model F-value of 201.91. This phenomenon confirms the significance of the model. The Predicted R2 of 0.9038 agrees reasonably with the Adjusted R2 of 0.9853. The findings suggest that the optimum concentration is 0.11 vol%, with an absorbance value of 0.990206 and a desirability level of 1.000

Artificial Bee Colony with Different Mutation Schemes: A comparative study

Artificial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR). The results on standard benchmark function suggest that the use of this operator improves performance in terms of convergence speed and quality of final obtained solution. It shows that Power and Polynomial mutations give best results. The fastest convergence was for the mutation rate value (MR=0.2)

Pengembangan Kawasan Perdesaan Berbasis Tanaman Pangan di Kecamatan Amuntai Utara Kabupaten Hulu Sungai Utara

       This study aims to determine (i) the comparative advantage of food crop commodities and food crop base sectors, (ii) competitive advantages of food crops and competitive and non-competitive areas, (ii) strengths, weaknesses, opportunities, and threats of rural food-based rural development in Amuntai Utara District, Hulu Sungai Utara Regency. The study was conducted on November 2018 - January 2019 in Amuntai Utara District Hulu Sungai Utara Regency with the research method used was descriptive qualitative and quantitative methods. Based on the results of the study, the LQ value in the North Amuntai Subdistrict has a base sector and non-base sector in food crop commodities; shift-share analysis shows that competitive commodities of rice food plants with a value of 0.12 and corn with a value of 0.56; SWOT analysis was obtained from an analysis of internal factors (IFAS) of 2,832 and external factor analysis (EFAS) of 2,45, hence the development of rural areas based on food crops was in quadrant I (supporting aggressive strategies

Unraveling the Genetic Architecture of Obesity: A Path to Personalized Medicine

Obesity is a global health challenge characterized by significant heterogeneity in causes and treatment responses, complicating sustainable management. This narrative review explores the genomic architecture of obesity and its implications for personalized interventions, focusing on how genetic variations influence key biological pathways and treatment outcomes. A comprehensive literature search, guided by the authors’ expertise, was conducted to identify key publications on the genomics of obesity and personalized approaches. The selection of articles prioritized those that provided direct insights into the genomic basis of obesity and its potential for informing tailored strategies. Genomic studies reveal both monogenic and polygenic influences on obesity, identifying numerous susceptibility loci. Genome-wide association studies (GWASs) have linked common variants in genes like FTO and MC4R to increased BMI and appetite dysregulation, respectively. Epigenetic research highlights the role of DNA methylation and other modifications in gene–environment interactions. Genetic and polygenic risk scores (GRSs and PRSs) show potential for refining risk stratification and predicting treatment response. The gut microbiome and metabolome also contribute to obesity pathogenesis, offering novel targets for intervention. Personalized medicine offers significant potential for improving obesity management through tailored interventions based on an individual’s genetic and ‘omics’ profile. Future research should focus on elucidating the functional consequences of identified variants, exploring gene–environment interactions, and developing strategies to overcome current limitations in clinical translation. With continued advancements, precision medicine can enhance treatment efficacy, increase sustainability, and help reduce the global burden of obesity-related diseases

Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review

Purpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and related terms. Studies focusing on AI’s role in obesity research, management, and therapeutic interventions were reviewed, including observational studies, systematic reviews, and clinical applications. Results: This review identifies numerous AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies. Applications of AI in obesity research include risk prediction, early detection, and individualization of treatment plans. AI has facilitated the development of predictive models utilizing various data sources, such as genetic, epigenetic, and clinical data. However, AI models vary in effectiveness, influenced by dataset type, research goals, and model interpretability. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated to optimize model selection. Conclusions: AI offers promising advancements in obesity management, enabling more personalized and efficient care. While technology presents considerable potential, challenges such as data quality, ethical considerations, and technical requirements remain. Addressing these will be essential to fully harness AI’s potential in obesity research and treatment, supporting a shift toward precision healthcare